The race for bigger language models continues, but the real power of modern AI is context. Specifically, it's about getting the right data to the right model at the right time.
In November 2024, Anthropic released the Model Context Protocol (MCP), an open standard for connecting AI systems to external tools and data sources. A year later, MCP has become the de facto standard for agentic AI, with adoption from OpenAI, Google, Microsoft, and thousands of developers building production integrations.
Here's what you need to know about MCP and why it matters for anyone building AI-powered applications.
The Problem MCP Solves
Every language model has a training cutoff date. No matter how capable the model, it can't know about events after that date, access your internal systems, or interact with external tools without help. The model itself is just the reasoning engine—the real utility comes from what you can connect it to.
Before MCP, connecting AI models to external tools meant building custom integrations for each combination of model and tool. Want Claude to access your database? Build an integration. Want GPT-4 to read your Slack messages? Build another one.
OpenAI tried to solve this with ChatGPT plugins in 2023, but the implementation was clunky and the ecosystem never took off. Anthropic watched, learned, and took a different approach: instead of building a proprietary plugin system, they created an open protocol that any model could use.
How MCP Works
MCP establishes a standardized way for AI models (clients) to communicate with external tools and data sources (servers). Think of it as REST for AI integrations.
The architecture has three main components:
- MCP Hosts: Applications that embed AI models and need to access external tools. Claude Desktop, VS Code with Copilot, and custom AI applications can all act as hosts.
- MCP Servers: Lightweight services that expose specific capabilities like file access, database queries, API calls, web scraping. These can run locally or in the cloud.
- The Protocol: A JSON-RPC-based communication standard that defines how hosts discover server capabilities, request actions, and receive responses.
When you configure an MCP server, you're essentially telling the AI: "Here's a tool you can use. Here's what it does. Here are the parameters it accepts." The model can then intelligently decide when and how to use that tool based on user requests.
A Simple Example
Say you want Claude to be able to search the web. You'd configure the Brave Search MCP server, which exposes a web_search tool. When a user asks Claude "What's the latest news about Python 3.13?", Claude recognizes this requires current information, calls the web_search tool with appropriate parameters, receives the results, and synthesizes them into a response.
The same pattern works for any capability: reading files, querying databases, calling APIs, running code, interacting with GitHub. Anything you can wrap in an MCP server becomes available to any MCP-compatible model.
The Ecosystem Exploded
Since launching in November 2024, MCP adoption has been remarkable:
- March 2025: OpenAI officially adopted MCP, integrating it across ChatGPT Desktop and their API
- April 2025: Google DeepMind confirmed MCP support for Gemini models
- 2025: Microsoft, AWS, Cloudflare, and Bloomberg joined as backers
- 97+ million monthly SDK downloads across Python, TypeScript, and other languages
- Thousands of community-built MCP servers for everything from Slack to Salesforce
In late 2025, Anthropic donated MCP to the Linux Foundation's new Agentic AI Foundation, where it joins projects from OpenAI and Block. This move signals that MCP isn't just Anthropic's protocol anymore, it's industry infrastructure.
Getting Started with MCP
The fastest way to experiment with MCP is through Claude Desktop. Here's a minimal setup:
1. Install Claude Desktop
Download from claude.ai/download. Works on macOS, Windows, and Linux.
2. Configure an MCP Server
Create or edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on your platform:
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/yourname/projects"
]
}
}
}This configures the filesystem MCP server to give Claude access to your projects directory.
3. Restart Claude Desktop
After restarting, you'll see available MCP tools in the interface. Claude can now read, write, and navigate files in the configured directory.
Building Custom Servers
Anthropic provides SDKs for Python and TypeScript. A minimal Python server looks like this:
from mcp.server import Server
from mcp.types import Tool, TextContent
server = Server("my-tool")
@server.tool()
async def get_weather(city: str) -> str:
"""Get current weather for a city."""
# Your implementation here
return f"Weather data for {city}"
if __name__ == "__main__":
server.run()The SDK handles protocol compliance, capability negotiation, and communication and you just implement the business logic.
Security Considerations
MCP's power comes with responsibility. In April 2025, security researchers identified several concerns that developers should understand:
- Prompt injection: Malicious content in tool responses could influence model behavior
- Tool combination risks: Combining tools (e.g., web scraping + file writing) could enable data exfiltration
- Lookalike tools: Malicious servers could impersonate trusted tools
- Permission scope: Overly broad permissions (like full filesystem access) increase attack surface
Best practices include: limiting tool permissions to what's actually needed, validating tool responses before acting on them, using server identity verification (added in the November 2025 spec), and maintaining audit logs of tool usage in production systems.
What This Means for Developers
MCP changes the economics of AI integration. Instead of building and maintaining custom integrations for each model-tool combination, you build once and work everywhere. As more models adopt MCP, your integrations become more valuable, not less.
For teams building AI-powered applications, MCP provides a clear path forward:
- Start with existing servers: The community has built MCP servers for most common tools. Check the official repository before building your own.
- Build custom servers for proprietary systems: Your internal databases, APIs, and tools can become MCP servers, making them accessible to any compatible AI.
- Design for composability: MCP servers that do one thing well can be combined to handle complex workflows.
- Plan for security: Treat MCP servers like any other API surface: authenticate, authorize, audit, and limit permissions.
If you're evaluating how to assess LLM capabilities for your applications, MCP compatibility should be on your checklist. And if you're building with frameworks like Pydantic.ai for type-safe AI agents, MCP provides the tool integration layer those agents need.
The Bottom Line
MCP represents a maturation of the AI ecosystem. Instead of fragmented, proprietary integrations, we're moving toward a world where AI tools are interoperable by default. For developers, this means less plumbing and more focus on what matters: building applications that solve real problems.
The fact that OpenAI, Google, Microsoft, and Anthropic are all backing the same standard is remarkable, and a signal that the industry recognizes interoperability benefits everyone. If you're building AI-powered applications, MCP isn't optional anymore. It's the foundation.
Need help integrating AI capabilities into your applications? We've been building production AI systems since the early days of the wave. Let's talk about your project.
